Bacterium such as Escherichia coli (E. coli) show biased Brownian motion in different chemical concentration gradients. This chemical sensitive motility or chemotaxis has gained considerable interest among scientists for some remarkable features such as chemo-sensory dynamic range, adaptation, diffusion and drift. A Boolean model of the whole chemotaxis process has been developed in this manuscript. The response of the circuit is in accordance with the experimental results available in the literature, providing indirect validation of the model. This simple Boolean network (BN) can be easily integrated into the paradigm of modular whole cell modelling. Another crucial application is in designing bio-inspired micro-robots to detect certain spatio-temporal chemical signatures.
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http://dx.doi.org/10.1109/EMBC.2016.7592126 | DOI Listing |
Health Promot Pract
January 2025
BASTA Coalition of Washington, Seattle, WA, USA.
Workplace sexual harassment (WSH) and other forms of sexual violence are pervasive in the agricultural sector, yet remain overlooked as critical occupational health and safety concerns. In this scoping review, the social-ecological model was used as a framework to examine contributing and protective factors in the literature that inform WSH interventions, policy, and research. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols, the authors searched eight databases using Boolean terms related to "sexual harassment" and "agriculture.
View Article and Find Full Text PDFLife (Basel)
December 2024
Department of Interdisciplinary Medicine, University of Bari "Aldo Moro", 70121 Bari, Italy.
Aim: Obstructive sleep apnea (OSA) is the most prevalent sleep-related breathing disorder. OSA affects approximately 2 million Italians, although only 3% receive a diagnosis and correct treatment. This review aims to provide an overview to guide clinical decision making, ensuring that patients receive the most appropriate treatment for their specific condition.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Can we turn AI black boxes into code? Although this mission sounds extremely challenging, we show that it is not entirely impossible by presenting a proof-of-concept method, MIPS, that can synthesize programs based on the automated mechanistic interpretability of neural networks trained to perform the desired task, auto-distilling the learned algorithm into Python code. We test MIPS on a benchmark of 62 algorithmic tasks that can be learned by an RNN and find it highly complementary to GPT-4: MIPS solves 32 of them, including 13 that are not solved by GPT-4 (which also solves 30). MIPS uses an integer autoencoder to convert the RNN into a finite state machine, then applies Boolean or integer symbolic regression to capture the learned algorithm.
View Article and Find Full Text PDFISA Trans
January 2025
School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, Jiangxi, China; Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang, 330013, Jiangxi, China. Electronic address:
Traditional data-driven models for predicting rare earth component content are primarily developed by relying on supervised learning methods, which suffer from limitations such as a lack of labeled data, lagging, and poor usage of a major amount of unlabeled data. This paper proposes a novel prediction approach based on the BiLSTM-Deep autoencoder enhanced traditional LSSVM algorithm, termed BiLSTM-DeepAE-LSSVM. This approach thoroughly exploits the implicit information contained in copious amounts of unlabeled data in the rare earth production process, thereby improving the traditional supervised prediction method and increasing the accuracy of component content predictions.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
Institute for Life Sciences and the Environment, University of Southern Queensland, Toowoomba, Queensland, 4350, Australia.
Military operations have long been recognized to cause significant environmental consequences. However, research on the environmental impacts of military operations remains fragmented despite the rise of modern technologies, including remote sensing (RS) and geographic information system (GIS). Hence, this study sought to review the literature on using RS and GIS approaches to assess military operations' environmental impacts.
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